Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders.


Journal

NeuroImage. Clinical
ISSN: 2213-1582
Titre abrégé: Neuroimage Clin
Pays: Netherlands
ID NLM: 101597070

Informations de publication

Date de publication:
2019
Historique:
received: 30 08 2018
revised: 30 12 2018
accepted: 14 01 2019
pubmed: 2 2 2019
medline: 4 1 2020
entrez: 2 2 2019
Statut: ppublish

Résumé

Current diagnostic systems for neurodevelopmental disorders do not have clear links to underlying neurobiology, limiting their utility in identifying targeted treatments for individuals. Here, we aimed to investigate differences in functional brain network integrity between traditional diagnostic categories (autism spectrum disorder [ASD], attention-deficit/hyperactivity disorder [ADHD], typically developing [TD]) and carefully consider the impact of comorbid ASD and ADHD on functional brain network integrity in a sample adequately powered to detect large effects. We also assess the neurobiological separability of a novel, potential alternative categorical scheme based on behavioral measures of executive function. Five-minute resting-state fMRI data were obtained from 168 children (128 boys, 40 girls) with ASD, ADHD, comorbid ASD and ADHD, and TD children. Independent component analysis and dual regression were used to compute within- and between-network functional connectivity metrics at the individual level. No significant group differences in within- or between-network functional connectivity were observed between traditional diagnostic categories (ASD, ADHD, TD) even when stratified by comorbidity (ASD + ADHD, ASD, ADHD, TD). Similarly, subgroups classified by executive functioning levels showed no group differences. Using clinical diagnosis and behavioral measures of executive function, no differences in functional connectivity were observed among the categories examined. Despite our limited ability to detect small- to medium-sized differences between groups, this work contributes to a growing literature suggesting that traditional diagnostic categories do not define neurobiologically separable groups. Future work is necessary to ascertain the validity of the executive function-based nosology, but current results suggest that nosologies reliant on behavioral data alone may not lead to discovery of neurobiologically distinct categories.

Sections du résumé

BACKGROUND
Current diagnostic systems for neurodevelopmental disorders do not have clear links to underlying neurobiology, limiting their utility in identifying targeted treatments for individuals. Here, we aimed to investigate differences in functional brain network integrity between traditional diagnostic categories (autism spectrum disorder [ASD], attention-deficit/hyperactivity disorder [ADHD], typically developing [TD]) and carefully consider the impact of comorbid ASD and ADHD on functional brain network integrity in a sample adequately powered to detect large effects. We also assess the neurobiological separability of a novel, potential alternative categorical scheme based on behavioral measures of executive function.
METHOD
Five-minute resting-state fMRI data were obtained from 168 children (128 boys, 40 girls) with ASD, ADHD, comorbid ASD and ADHD, and TD children. Independent component analysis and dual regression were used to compute within- and between-network functional connectivity metrics at the individual level.
RESULTS
No significant group differences in within- or between-network functional connectivity were observed between traditional diagnostic categories (ASD, ADHD, TD) even when stratified by comorbidity (ASD + ADHD, ASD, ADHD, TD). Similarly, subgroups classified by executive functioning levels showed no group differences.
CONCLUSIONS
Using clinical diagnosis and behavioral measures of executive function, no differences in functional connectivity were observed among the categories examined. Despite our limited ability to detect small- to medium-sized differences between groups, this work contributes to a growing literature suggesting that traditional diagnostic categories do not define neurobiologically separable groups. Future work is necessary to ascertain the validity of the executive function-based nosology, but current results suggest that nosologies reliant on behavioral data alone may not lead to discovery of neurobiologically distinct categories.

Identifiants

pubmed: 30708240
pii: S2213-1582(19)30028-2
doi: 10.1016/j.nicl.2019.101678
pmc: PMC6356009
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

101678

Subventions

Organisme : NCRR NIH HHS
ID : UL1 RR025005
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH107549
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH085328
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH078160
Pays : United States
Organisme : NIMH NIH HHS
ID : K01 MH109766
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS048527
Pays : United States

Informations de copyright

Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

Auteurs

Dina R Dajani (DR)

Department of Psychology, University of Miami, Coral Gables, FL, United States. Electronic address: dina.r.dajani@gmail.com.

Catherine A Burrows (CA)

Institute on Community Integration, University of Minnesota, Minneapolis, MN, United States.

Paola Odriozola (P)

Department of Psychology, Yale University, New Haven, CT, United States.

Adriana Baez (A)

Department of Psychology, University of Miami, Coral Gables, FL, United States.

Mary Beth Nebel (MB)

Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.

Stewart H Mostofsky (SH)

Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States.

Lucina Q Uddin (LQ)

Department of Psychology, University of Miami, Coral Gables, FL, United States; Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, United States.

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Classifications MeSH